A Formal Approach to Modeling the Memory of a Living Organism

TL;DR

This paper presents a formal, multi-level model of living organism memory that integrates biological relevance assessment with an algorithmic database updating process, explaining human memory phenomena and enabling learning problem definitions.

Contribution

It introduces a novel formal framework combining biological and computational aspects of memory, modeling sensory processing, relevance evaluation, and dynamic database updates.

Findings

Model explains human memory properties like reasoning and errors.

Demonstrates the model's computational efficiency and flexibility.

Shows potential for implementing as neuron-like networks.

Abstract

We consider a living organism as an observer of the evolution of its environment recording sensory information about the state space X of the environment in real time. Sensory information is sampled and then processed on two levels. On the biological level, the organism serves as an evaluation mechanism of the subjective relevance of the incoming data to the observer: the observer assigns excitation values to events in X it could recognize using its sensory equipment. On the algorithmic level, sensory input is used for updating a database, the memory of the observer whose purpose is to serve as a geometric/combinatorial model of X, whose nodes are weighted by the excitation values produced by the evaluation mechanism. These values serve as a guidance system for deciding how the database should transform as observation data mounts. We define a searching problem for the proposed model and discuss the model's flexibility and its computational efficiency, as well as the possibility of implementing it as a dynamic network of neuron-like units. We show how various easily observable properties of the human memory and thought process can be explained within the framework of this model. These include: reasoning (with efficiency bounds), errors, temporary and permanent loss of information. We are also able to define general learning problems in terms of the new model, such as the language acquisition problem.